1. Field of the Invention
The disclosures herein generally relate to an information processing system, an information processing apparatus, and an information processing method.
2. Description of the Related Art
Conventionally, a method of machine learning has been known that distinguishes whether data represents an abnormal value (an outlier), and a technology has been known that detects, for example, an irregular product and a defective product by using such a method. Methods of distinguishing whether data represents an abnormal value are generally classified into three methods of the supervised anomaly detection method, the semi-supervised anomaly detection method, and the unsupervised anomaly detection method.
Here, a conventional technology has been known that classifies (distinguishes) input data, by using a supervised anomaly detection method in a circumstance where a sufficient amount of learning data cannot be not obtained (see, for example, Patent Document 1).
However, since the conventional technology uses a supervised anomaly detection method, there may be a problem in that precision may be low for a distinguished result if it is difficult to obtain data that represents an abnormal value as learning data. Namely, for example, as learning data for distinguishing certain products, if a considerable amount of data can be obtained that represents normal values, but data that represents abnormal values can be hardly obtained, precision may be low for the distinguished result because learning has not been sufficiently executed for data that represents abnormal values.
On the other hand, the semi-supervised anomaly detection method is a method that uses data that represents normal values as learning data, by which it is often the case that precision of a distinguished result is lower compared to the supervised anomaly detection method in general, yet it has an advantage that an unexpected abnormal value can be detected.
In view of the above, it is a general object of at least one embodiment of the present invention to distinguish data with high precision, by using the semi-supervised anomaly detection method.